Data Sets on Pensions and Health: Data Collection and Sharing for Policy Design

Introduction. Prior to 1980, U.S. national demographic and health data collection did not identify individuals of Hispanic/Latina/o heritage as a population group. Post-1990, robust immigration from Latin America (e.g., South America, Central America, Mexico) and subsequent growth in U.S. births, dynamically reconstructed the ethnoracial lines among Latinos from about 20 countries, increasing racial admixture and modifying patterns of health disparities. The increasing racial and class heterogeneity of U.S. Latina/os demands a critical analysis of sociodemographic factors associated with population health disparities. Purposes. To determine the state of available Latina/o population demographic and health data in the United States, assess demographic and health variables and trends from 1960 to the present, and identify current strengths, gaps, and areas of improvement. Method. Analysis of 101 existing data sets that included demographic, socioeconomic, and health characteristics of the U.S. Latina/o population, grouped by three, 20-year intervals: 1960–1979, 1980–1999, and 2000–2019. Results. Increased Latina/o immigration and U.S. births between 1960 and 2019 was associated with increases of Latino population samples in data collection. Findings indicate major gaps in the following four areas: children and youth younger than 18 years, gender and sexual identity, race and mixed-race measures, and immigration factors including nativity and generational status. Conclusions. The analysis of existing ethnoracial Latina/o population data collection efforts provides an opportunity for critical analysis of past trends, future directions in data collection efforts, and an equity lens to guide appropriate community health interventions and policies that will contribute to decreasing health disparities in Latina/o populations.

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Methodological issues to analyse real-world longitudinal occupational health data (Preprint)

10.2196/preprints.35068 ◽

2021 ◽

Author(s):

Rémi Colin Chevalier ◽

Frédéric Dutheil ◽

Samuel Dewavrin ◽

Thomas Cornet ◽

Julien S Baker ◽

...

Keyword(s):

Data Collection ◽

Occupational Health ◽

Longitudinal Data ◽

Real World ◽

Health Data ◽

Correlated Data ◽

Structure Identification ◽

Data Sets ◽

Methodological Issues ◽

Imputation Methods

UNSTRUCTURED Ever greater technological advances and democratization of digital tools such as computers and smartphones offer researchers new possibilities to collect large amounts of health data in order to conduct clinical research. Such data, called real-world data (RWD), appears to be a perfect complement to traditional randomized clinical trials (RCTs) and has become more important in health decisions. Due to its longitudinal nature, RWD is subject to well-known methodological issues that can occur when collecting this type of data. In this article, we present the three main methodological problems encountered by researchers, these include, the longitudinal data itself, missing data (not available - NA) and cluster-correlated data. These concepts have been widely discussed in the literature and many methods and solutions have been proposed to cope with these issues. As examples, mixed and trajectory models have been developed to explore longitudinal data sets, imputation methods can resolve NA issues, and multilevel models facilitate treating cluster-correlated data. This article reviews the various solutions proposed and attempts to analyze all three in detail. Although solutions exist to meet these data collection challenges, solutions are not always correctly exploited, especially in cases where data collection issues overlap. In an attempt to solve this problem, we have conceived a process that considers all three issues simultaneously. This process can be divided into two parts: the first part of data management comprises of several phases such as definition of data structure, identification of suspect data and application of imputation methods. The second part of the analysis relates to the application of different models for repeated data using the modified data set. As a result, it should be possible to facilitate work with data sets and provide results with higher confidence levels. To support our proposal, we have used results from the “Wittyfit” database, which is an epidemiological database of occupational health data.

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Participant recruitment and data collection through Facebook: the role of personality factors

10.31234/osf.io/qg3rt ◽

2017 ◽

Author(s):

Sean Chandler Rife ◽

Kelly L. Cate ◽

Michal Kosinski ◽

David Stillwell

Keyword(s):

Data Collection ◽

Social Research ◽

Personality Factors ◽

The Internet ◽

Data Sets ◽

Social Networking Service ◽

Participant Recruitment ◽

Conducting Research ◽

Promising Avenue

As participant recruitment and data collection over the Internet have become more common, numerous observers have expressed concern regarding the validity of research conducted in this fashion. One growing method of conducting research over the Internet involves recruiting participants and administering questionnaires over Facebook, the world’s largest social networking service. If Facebook is to be considered a viable platform for social research, it is necessary to demonstrate that Facebook users are sufficiently heterogeneous and that research conducted through Facebook is likely to produce results that can be generalized to a larger population. The present study examines these questions by comparing demographic and personality data collected over Facebook with data collected through a standalone website, and data collected from college undergraduates at two universities. Results indicate that statistically significant differences exist between Facebook data and the comparison data-sets, but since 80% of analyses exhibited partial η2 < .05, such differences are small or practically nonsignificant in magnitude. We conclude that Facebook is a viable research platform, and that recruiting Facebook users for research purposes is a promising avenue that offers numerous advantages over traditional samples.

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A framework for federal health data collection

10.1145/1499799.1499909 ◽

1976 ◽

Author(s):

N. Phillip Ross ◽

Meyer Katzper

Keyword(s):

Data Collection ◽

Health Data

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Getting Started Creating Data Dictionaries: How to Create a Shareable Data Set

Advances in Methods and Practices in Psychological Science ◽

10.1177/2515245920928007 ◽

2021 ◽

Vol 4 (1) ◽

pp. 251524592092800

Author(s):

Erin M. Buchanan ◽

Sarah E. Crain ◽

Ari L. Cunningham ◽

Hannah R. Johnson ◽

Hannah Stash ◽

...

Keyword(s):

Data Collection ◽

Data Sharing ◽

Search Engine ◽

Web Applications ◽

Data Sets ◽

Data Dictionary ◽

Data Set ◽

Entire Process ◽

Shared Data ◽

Source Data

As researchers embrace open and transparent data sharing, they will need to provide information about their data that effectively helps others understand their data sets’ contents. Without proper documentation, data stored in online repositories such as OSF will often be rendered unfindable and unreadable by other researchers and indexing search engines. Data dictionaries and codebooks provide a wealth of information about variables, data collection, and other important facets of a data set. This information, called metadata, provides key insights into how the data might be further used in research and facilitates search-engine indexing to reach a broader audience of interested parties. This Tutorial first explains terminology and standards relevant to data dictionaries and codebooks. Accompanying information on OSF presents a guided workflow of the entire process from source data (e.g., survey answers on Qualtrics) to an openly shared data set accompanied by a data dictionary or codebook that follows an agreed-upon standard. Finally, we discuss freely available Web applications to assist this process of ensuring that psychology data are findable, accessible, interoperable, and reusable.

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A new risk-adjusted Bernoulli cumulative sum chart for monitoring binary health data

Statistical Methods in Medical Research ◽

10.1177/0962280214530883 ◽

2016 ◽

Vol 25 (6) ◽

pp. 2704-2713 ◽

Cited By ~ 5

Author(s):

Giuseppe Rossi ◽

Simone Del Sarto ◽

Marco Marchi

Keyword(s):

Simple Formula ◽

Efficient Solution ◽

Real Data ◽

Health Data ◽

Data Sets ◽

Cumulative Sum ◽

Baseline Incidence ◽

Health Event ◽

Control Limits ◽

Underlying Risk

To monitor a health event in patients with a specific risk of developing the event, a risk-adjusted cumulative sum chart is needed. The risk-adjusted cumulative sum chart proposed in the literature has some limitations. Setting appropriate control limits is not straightforward, there is no simple formula for constructing them, and they remain sensitive to changes in the underlying risk distribution and the baseline incidence rate. To overcome these limits, we propose a new risk-adjusted Bernoulli cumulative sum chart as a simple and efficient solution. Analyses of simulated and real data sets illustrate the performance and usefulness of the proposed procedure.

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Opportunities and Challenges in Public Health Data Collection in Southern Asia: Examples from Western India and Kathmandu Valley, Nepal

Sustainability ◽

10.3390/su9071106 ◽

2017 ◽

Vol 9 (7) ◽

pp. 1106 ◽

Cited By ~ 6

Author(s):

Amruta Nori-Sarma ◽

Anobha Gurung ◽

Gulrez Azhar ◽

Ajit Rajiva ◽

Dileep Mavalankar ◽

...

Keyword(s):

Public Health ◽

Data Collection ◽

Health Data ◽

Kathmandu Valley ◽

Western India ◽

Southern Asia ◽

Public Health Data

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